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ICML
2008
IEEE
14 years 8 months ago
Learning to classify with missing and corrupted features
After a classifier is trained using a machine learning algorithm and put to use in a real world system, it often faces noise which did not appear in the training data. Particularl...
Ofer Dekel, Ohad Shamir
AROBOTS
2008
158views more  AROBOTS 2008»
13 years 8 months ago
Learning traversability models for autonomous mobile vehicles
Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability of the terrain so that they can effectively plan thei...
Michael Shneier, Tommy Chang, Tsai Hong, William P...
BMCBI
2010
143views more  BMCBI 2010»
13 years 8 months ago
Learning gene regulatory networks from only positive and unlabeled data
Background: Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled...
Luigi Cerulo, Charles Elkan, Michele Ceccarelli
ICRA
2005
IEEE
122views Robotics» more  ICRA 2005»
14 years 1 months ago
Supervised Learning of Places from Range Data using AdaBoost
— This paper addresses the problem of classifying places in the environment of a mobile robot into semantic categories. We believe that semantic information about the type of pla...
Óscar Martínez Mozos, Cyrill Stachni...
JCIT
2010
148views more  JCIT 2010»
13 years 2 months ago
Investigating the Performance of Naive- Bayes Classifiers and K- Nearest Neighbor Classifiers
Probability theory is the framework for making decision under uncertainty. In classification, Bayes' rule is used to calculate the probabilities of the classes and it is a bi...
Mohammed J. Islam, Q. M. Jonathan Wu, Majid Ahmadi...